Imports

from utils import print_time_report, print_results, plot_results

Time report

print_time_report()

	hour	min	sec
algo
KNeighborsClassifier	0	6	7
daal4py_KNeighborsClassifier	0	1	20
KNeighborsClassifier_kd_tree	0	1	19
daal4py_KNeighborsClassifier_kd_tree	0	0	50
KMeans	0	9	37
daal4py_KMeans	0	2	55
total	0	22	11

KNeighborsClassifier - Brute Force

print_results(algo="KNeighborsClassifier", versus_lib="daal4py")

plot_results(algo="KNeighborsClassifier", versus_lib="daal4py", group_by_cols=["algorithm", "n_neighbors", "function"], split_hist_by=["n_jobs"])

KNeighborsClassifier - KD Tree

print_results(algo="KNeighborsClassifier_kd_tree", versus_lib="daal4py")

plot_results(algo="KNeighborsClassifier_kd_tree", versus_lib="daal4py", group_by_cols=["algorithm", "n_neighbors", "function"], split_hist_by=["n_jobs"])

KMeans

from utils import _make_dataset
data = _make_dataset('KMeans', 'daal4py', compare_cols=["n_iter"])

data[["estimator", "lib", "function", "n_samples", "n_features", "n_iter_sklearn", "n_iter_daal4py"]].sort_values(["n_iter_sklearn", "n_iter_daal4py"])

	estimator	lib	function	n_samples	n_features	n_iter_sklearn	n_iter_daal4py
0	KMeans	sklearn	fit	10000	2	30.0	22.0
9	KMeans	sklearn	fit	10000	2	30.0	30.0
18	KMeans	sklearn	fit	10000	100	30.0	30.0
27	KMeans	sklearn	fit	10000	100	30.0	30.0
36	KMeans	sklearn	fit	1000000	2	30.0	30.0
...	...	...	...	...	...	...	...
67	KMeans	sklearn	predict	1000	100	NaN	NaN
68	KMeans	sklearn	predict	1	100	NaN	NaN
69	KMeans	sklearn	fit	1000000	100	NaN	NaN
70	KMeans	sklearn	predict	1000	100	NaN	NaN
71	KMeans	sklearn	predict	1	100	NaN	NaN

72 rows × 7 columns

print_results(algo="KMeans", versus_lib="daal4py", compare_cols=["n_iter"])

plot_results(algo="KMeans", versus_lib="daal4py", group_by_cols=["init", "max_iter", "n_clusters", "n_init", "tol", "function"], compare_cols=["n_iter"])